This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint.
Downloads
Supplementary Files
Authors
Abstract
The global food trade system is resilient to minor disruptions but vulnerable to major ones. Major shocks can arise from global catastrophic risks, such as abrupt sunlight reduction scenarios (e.g., nuclear war) or global catastrophic infrastructure loss (e.g., due to severe geomagnetic storms or a global pandemic). We use a network model to examine how these two scenarios could impact global food trade, focusing on wheat, maize, soybeans, and rice, accounting for about 60% of global calorie intake. Our findings indicate that an abrupt sunlight reduction scenario, with soot emissions equivalent to a major nuclear war between India and Pakistan (37 Tg), could severely disrupt trade, causing most countries to lose the vast majority of their food imports (50-100 % decrease), primarily due to the main exporting countries being heavily affected. Global catastrophic infrastructure loss of the same magnitude as the abrupt sunlight reduction has a more homogeneous distribution of yield declines, resulting in most countries losing up to half of their food imports (25-50 % decrease). Thus, our analysis shows that both scenarios could significantly impact the food trade. However, the abrupt sunlight reduction scenario is likely more disruptive than global catastrophic infrastructure loss regarding the effects of yield reductions on food trade. This study underscores the vulnerabilities of the global food trade network to catastrophic risks and the need for enhanced preparedness.
DOI
https://doi.org/10.31223/X5MQ4R
Subjects
Agriculture, Human Geography, International and Area Studies, Nature and Society Relations, Other Geography
Keywords
Food Trade, Trade, agriculture, Food production, yield shock, Global Catastrophic Infrastructure Loss, Abrupt Sunlight Reduction Scenario, Geomagnetic Storm, Nuclear War, food security
Dates
Published: 2024-06-29 03:53
Last Updated: 2024-10-01 13:58
Older Versions
License
CC BY Attribution 4.0 International
Additional Metadata
Data Availability (Reason not available):
All data and code used for this study are available in the Github repository: https://github.com/allfed/pytradeshifts
There are no comments or no comments have been made public for this article.